class_docstr: General class for handling a Gaussian Process in GPyOpt.
  
  :param kernel: GPy kernel to use in the GP model.
  :param noise_var: value of the noise variance if known.
  :param exact_feval: whether noiseless evaluations are available. IMPORTANT to make the optimization work well in noiseless scenarios (default, False).
  :param optimizer: optimizer of the model. Check GPy for details.
  :param max_iters: maximum number of iterations used to optimize the parameters of the model.
  :param optimize_restarts: number of restarts in the optimization.
  :param sparse: whether to use a sparse GP (default, False). This is useful when many observations are available.
  :param num_inducing: number of inducing points if a sparse GP is used.
  :param verbose: print out the model messages (default, False).
  :param ARD: whether ARD is used in the kernel (default, False).
  :param mean_function: GPy Mapping to use as the mean function for the GP model (default, None).
  
  .. Note:: This model does Maximum likelihood estimation of the hyper-parameters.
code: def __init__(
      self,
      kernel=None,
      noise_var=None,
      exact_feval=False,
      optimizer="bfgs",
      max_iters=1000,
      optimize_restarts=5,
      sparse=False,
      num_inducing=10,
      verbose=True,
      ARD=False,
      mean_function=None,
  ):
      self.kernel = kernel
      self.noise_var = noise_var
      self.exact_feval = exact_feval
      self.optimize_restarts = optimize_restarts
      self.optimizer = optimizer
      self.max_iters = max_iters
      self.verbose = verbose
      self.sparse = sparse
      self.num_inducing = num_inducing
      self.model = None
      self.ARD = ARD
      self.mean_function = mean_function
  
imports: import numpy
  import GPy
  from .base import BOModel
label_desc: Use image node_id 1 to create a new GPModel object from inherited base classes: BOModel with example: obj = GPModel(kernel, noise_var, exact_feval, optimizer, max_iters, optimize_restarts, sparse, num_inducing, verbose, ARD, mean_function)